Weights & Biases
Track, visualize, and compare machine learning experiments, with newer Weave and Inference tools for evaluating and monitoring LLM-based applications.
The verdict on Weights & Biases: ML teams running many training experiments who need to compare runs without digging through raw logs Weights and Biases is the answer to a problem every serious ML team hits: you ran 40 training experiments last month and can't reliably remember why one configuration outperformed another. Pricing: Free / $60/mo Pro / Enterprise custom. Last reviewed: August 2026.
Best For
ML teams running many training experiments who need to compare runs without digging through raw logs
Standout Feature
Experiment tracking that answers exactly why one run outperformed another, permanently, not from memory
TL;DR
The industry standard for serious ML work, real overkill for a simple fine-tuning job or prompt project.
Alternatives
Overview
Weights & Biases is the MLOps platform that tracks every training run, logs metrics, compares experiments, visualizes model behavior, and manages the full ML lifecycle, from training to deployment to monitoring in production. Data scientists and ML engineers use it to answer 'why did this run perform better than that one' without digging through logs, to collaborate on experiment results across the team, and to build dashboards that show model performance over time. The recent LLM features add prompt management, evaluation datasets, and trace logging for production LLM applications.
Our Take
The experiment tracking is permanent, not from memory, and integrates with every major ML framework. The newer Weave and Inference tools extend that into LLM evaluation and monitoring. At $60 per month for the Pro plan, it's reasonably priced for ML teams running many training runs. It's real overkill for a one-off fine-tuning job or a prompt engineering project that never touches training. Use it when 'which run was that' is a recurring problem.
Key Features
- Experiment tracking
- Metric logging and visualization
- Model versioning
- Prompt management
- Dataset versioning
- Production monitoring
- • Experiment comparison eliminates the 'which run was that' problem permanently
- • Industry standard, integrations with every major ML framework
- • LLM features extend the platform to production application monitoring
- • Can be overkill for simple fine-tuning jobs or prompt engineering projects
- • Team pricing adds up quickly for large ML organizations
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Workflows Using This Tool
Step-by-step playbooks that put Weights & Biases to work.